Data Structure

Numeric Data Type in R Language

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The article is about Numeric Data Type in R Language. Decimal values are referred to as numeric data types in R, which is the default working out data type for numbers in R Language.

Numeric Data Type in R Language

Assigning a decimal value to a variable $x$ creates a variable that has a numeric data type. For example

x <- 6.2
print(x)

Since numeric data types consist of numbers, one can perform different mathematical operations such as addition, subtraction, multiplication, division, etc.

Class of Numeric Data Type

In R, the class of numeric variables is numeric. One can check the class of a numeric object ($x$) by using class() function.

class(x)
Numeric Data Type in R

Converting Character Type to Numeric Type in R

In R Language, the as.numeric() function is used to convert a vector of character values to a numeric value. Note that by default, R converts character vectors to factors.

One can confirm the data type of an object by using a function is.numeric(). For example,

is.numeric(x)

If is.numeric(x) results in an output of TRUE then it means that the data type of the variable/object $x$ is numeric. Let’s assign a whole number to a variable $y$ and then check the class of object $y$:

y <- 2
class(y)
[1] "numeric"

It means that the default data type for numbers is the numeric type in R Language. One can also use typeof() function to confirm the data type of a variable.

Creating Numeric Vectors

One can also create a variable (called a numeric vector) by using the numeric function in R. It will create a vector of zeros. For example,

z <- numeric(5)
print(z)

[1] 0 0 0 0 0

class(z)

[1] "numeric"


Other methods also exist for the creation of numeric vectors. Note that the numeric data type is different from the integer.

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In Summary, the numeric data type in R is a fundamental data structure for numerical computations in R. Understanding its properties and when to potentially use the integer data type is essential for effective data analysis in R.

Frequently Asked Questions About R Numeric Data type in R

Data Frame in R Language

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Introduction to Data Frame in R Language

In R Programming language a data frame is a two-dimensional data structure. The data frame objects contain rows and columns. The number of rows for each column should have equal length. The cross-section of the row and column can be considered as a cell. Each cell of the data frame is associated with a combination of row number and column number.

A data frame in R Programming Langauge has:

  • Rows: Represent individual observations or data points.
  • Columns: Represent variables or features being measured. Each column holds values for a single variable across all observations.
  • Data Types: Columns can hold data of different types, including numeric, character, logical (TRUE/FALSE), and factors (categorical variables).

One can modify, extract, and re-arrange the data contents of a data frame; the process is called the manipulation of the data frame. To create a data frame a general syntax can be followed

Data Frame Syntax in R

The general syntax of a data frame in R Language is

df <- data.frame(first column = c(data values separated with commas,
                           second column = c(data values separate with commans,
                           ......
          )

An exemplary data frame in the R Programming language is

df = data.frame(age = c(23, 24, 25, 26, 23, 25, 29, 20),
                marks = c(99, 80, 67, 56, 98, 65, 45, 77),
                grade = c("A", "A", "C", "D", "A", "B", "F", "B")
                )
print(df)
Data Frame in R Language

One can name or rename the columns and rows of the data frame

# Naming / renaming columns 
colnames(df) <- c("Age", "Score", "Grad")

# Naming / renaming rows
row.names(df) <- c("1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th")
Data Frame in R Language colnames and row names

Subsetting a Data Frame

The subset() method can be used to create a new data set by removing specified column(s). This splits the data frame into two sets, one with excluded columns and the other with included columns. To understand subsetting a data frame, let us create a data frame first.

# creating a data frame
df = data.frame(row1 = 0:3, row2 = 3:6, row3 = 6:9)

# creating a subset
df <- subset(df, select = c(row1, row2))
subsetting a data frame

Question: Data Frame in R Language

Suppose we have a frequency distribution of sales from a sample of 100 sales receipts.

Price ValueNumber of Sales
0 to 2016
20 to 4018
40 to 6014
60 to 8024
80 to 10020
100 to 1208

Calculate the mean, median, variance, standard deviation, and coefficient of variation by using the R code.

Solution

# Crate a data frame 

df <- data.frame(lower_class = seq(0, 100, by = 20), upper_class=seq(20, 120, by=20), freq = c(16, 18, 14, 24, 20, 8))

# mid points
m <- (df["lower_class"] + df["upper_class"])/2

mf <- df["freq"] * m
mfsquare <- df["freq"] * m^2


data <- cbind(df, m, mf, mfsquare)
colnames(data) <- c("LL","UL", "freq" , "M", "mf", "mf2")

# Computation
avg = sum(data$mf)/sum(data$freq)
var = (sum(data$mf2) - sum(data$mf)^2 / sum(data$freq))/(sum(data$freq)-1)
sd = sqrt(var)
CV = sd/avg * 100

## Outputs
paste("Mean = ", round(avg, 3))
paste("Variance = ", round(var, 3))
paste("Standard Deviation = ", round(sd, 3))
paste("Coefficient of Variation = ", round(CV, 3))
Frequency Distribution and Descriptive Statistics

Using Logical Conditions for Selecting Rows and Columns

For selecting rows and columns using logical conditions, we consider the iris data set. Here, suppose we are interested in Selecting rows whose values are higher than the median for Sepal Length and whose Petal.Width >= 1.7. In the code below, each value is Sepal.Length variable (column) is compared with the median value of Sepal.Length. Similarly, each value of Petal.Width is compared with 1.7 to extract the required values from these two columns.

attach(iris) 

iris[(Sepal.Length > median(Sepal.Length) & Petal.Width >= 1.7), ]

One can select only the numeric columns from the data frame by following the code below

# Selecting Numeric Columns only
iris[ , sapply(iris, is.numeric)]

# Selecting factor columns only
iris[, sapply(iris, is.factor)]

# Selecting only certain Species
 iris[Species == "virginica", ]

Omitting Missing Observations in a Data Frame

# Omit rows with missing data
na.omit(iris)

# check for missing data across rows
apply(iris, 2, is.na)
iris[complete.cases(iris), ]

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Important R Language Questions

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The post is about R Language Questions that are commonly asked in interviews or R Language-related examinations and tests.

R Language Questions

Question: What is a file in R?
Answer: A script file written in R has a file extension of R. Since, R is a programming language designed to perform statistical computing and graphics on given data, that is why, a file in R contains code that can be executed within the R software environment.

Question: What is the table in R?
Answer: A table in R language is an arbitrary R object, that is inherited from the class “table” for the as.data.frame method. A table in R language refers to a data structure that is used to represent categorical data and frequency counts. A table provides a convenient way to summarize and organize the data into a tabular format, making it easier to analyze and interpret.

Factor Variables in R

Questions: What is the factor variable in R language?
Answer: Factor variables are categorical variables that hold either string or numeric values. The factor variables are used in various types of graphics, particularly for statistical modeling where the correct number of degrees of freedom is assigned to them.

Data Structure in R

Questions: What is Data Structure in R?
Answer: A data structure is a specialized format for organizing and storing data. General data structure types include the array, the file, the record, the table, the tree, and so on. R offers several data structures, each with its characteristics and purposes. In R common data structures are: vector, factor, matrix, array, data frame, and lists.

scan() Function in R

Question: What is a scan() in R?
Answer: The scan() in R is used to Read Data Values: Read data into a vector or list from the console or file. For Example:

Z <- scan()
1: 12 5
3: 2
4:
Read 3 items

> z
[1] 12 5 2
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readline() Function in R

Questions: What is readline() in R?
Answer: The deadline() function in R, read text lines from a Connection: Read some or all text lines from a connection. One can use readline() for inputting a line from the keyboard in the form of a string. For Example:

w <- readline()
xyz vw u
> w

[1] "xyz vw u"

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